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README.md

@@ -1,23 +1,33 @@
 # Tatar Speech Commands Dataset
 
-This dataset contains 3,547 one-second utterances of 35 commands commonly used in robotics, IoT, and smart systems.  The data was collected from 153 speakers.
+This dataset contains 3,547 one-second utterances of 35 commands commonly used in robotics, IoT, and smart systems.  The recordings were collected from 153 different speakers.  The data is suitable for training and evaluating speech command recognition models.
 
 ## Dataset Statistics
 
+* **Number of speakers:** 153
 * **Number of commands:** 35
 * **Number of utterances:** 3,547
-* **Number of speakers:** 153
 * **Audio length:** 1 second per utterance
 
 ## Data Download
 
 The dataset can be downloaded from [Google Drive](https://drive.google.com/file/d/1CBmVeAYgNrkNKhL1wtG7KUKuLJ9hOfHL/view?usp=sharing).
 
-## Related Work
+## Preprocessing and Augmentation
+
+A Jupyter Notebook (`data_preprocessing_augmentation.ipynb`) is provided for data preprocessing and augmentation.  This notebook requires the ESC-50 dataset ([https://github.com/karolpiczak/ESC-50](https://github.com/karolpiczak/ESC-50)).
+
+## Model
+
+The provided Keyword-MLP model ([https://github.com/AI-Research-BD/Keyword-MLP](https://github.com/AI-Research-BD/Keyword-MLP)) was used for training and testing on this dataset.
+
+##  Inference
+
+Inference can be performed using either PyTorch or ONNX runtime.  Scripts are provided for both short (approximately 1-second) audio clips and longer clips processed using a sliding window approach.
+
 
-The provided Keyword-MLP model ([https://github.com/AI-Research-BD/Keyword-MLP](https://github.com/AI-Research-BD/Keyword-MLP)) was used for training and testing on this dataset.  The TatarSCR repository ([https://github.com/IS2AI/TatarSCR.git](https://github.com/IS2AI/TatarSCR.git)) contains the code and configurations used in this work.  Preprocessing and augmentation can be performed using the `data_preprocessing_augmentation.ipynb` notebook, which requires the ESC-50 dataset ([https://github.com/karolpiczak/ESC-50](https://github.com/karolpiczak/ESC-50)).
+## License
 
+[Specify License here]
 
-## Model Inference
 
-Inference can be performed using PyTorch or ONNX.  PyTorch offers two scripts: `inference.py` for short audio clips and `window_inference.py` for longer clips using a sliding window approach.  ONNX inference is handled by `onnx_inference.py`.  The `label_map.json` file is required for inference.